{"id":18300488,"url":"https://github.com/leandromineti/ml-journey","last_synced_at":"2025-04-09T09:29:45.217Z","repository":{"id":217193564,"uuid":"279578166","full_name":"leandromineti/ml-journey","owner":"leandromineti","description":"A curated collection of free learning resources on topics related to Machine Learning.","archived":false,"fork":false,"pushed_at":"2023-01-27T14:08:40.000Z","size":95,"stargazers_count":24,"open_issues_count":0,"forks_count":5,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-02-15T03:41:16.136Z","etag":null,"topics":["artificial-intelligence","machine-learning","mathematics","statistics"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"unlicense","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/leandromineti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-07-14T12:24:32.000Z","updated_at":"2024-12-30T23:30:46.000Z","dependencies_parsed_at":"2024-01-15T00:54:46.333Z","dependency_job_id":"479ec812-4aff-437e-ab0e-3a8c773225d5","html_url":"https://github.com/leandromineti/ml-journey","commit_stats":null,"previous_names":["leandromineti/ml-journey"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leandromineti%2Fml-journey","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leandromineti%2Fml-journey/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leandromineti%2Fml-journey/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leandromineti%2Fml-journey/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/leandromineti","download_url":"https://codeload.github.com/leandromineti/ml-journey/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248012027,"owners_count":21033122,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","machine-learning","mathematics","statistics"],"created_at":"2024-11-05T15:12:37.452Z","updated_at":"2025-04-09T09:29:45.196Z","avatar_url":"https://github.com/leandromineti.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Journey\n\nThis repository contains a curated collection of **free** learning resources on topics related to Machine Learning. \nI tend to favor materials that explain concepts visually and promote a hands-on learning experience. \nThe references are organized as follows:\n- Math \u0026 Stats\n  - Fundamentals\n  - Linear Algebra\n  - Calculus\n  - Statistics\n- Computing\n  - Python\n  - R\n  - SQL\n  - NoSQL\n  - Git\n  - Cloud\n- Machine Learning\n  - Fundamentals\n  - Data-specific resources\n  - Model-specific resources\n  - Application-specific resources\n  - Digging deeper\n\n## Math \u0026 Stats\n\n### Fundamentals\n\n- [Khan Academy](https://www.khanacademy.org/) - Fundamental Math courses.\n- [Introduction to computational thinking for Data Science - MIT](https://www.youtube.com/playlist?list=PLUl4u3cNGP619EG1wp0kT-7rDE_Az5TNd) - Playlist.\n- [Mathematics for Machine Learning - Deisenroth *et al*](https://mml-book.github.io/) - Book.\n\n### Linear Algebra\n\n- [Essence of Linear Algebra - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) - Playlist.\n- [Gilbert Strang lectures on Linear Algebra - MIT](https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D) - Playlist.\n- [Introduction to Applied Linear Algebra - Boyd \u0026 Vandenberghe](http://vmls-book.stanford.edu/) - Book.\n\n### Calculus\n\n- [Essence of Calculus - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) - Playlist.\n- [The Matrix Calculus You Need For Deep Learning - Explained.ai](https://explained.ai/matrix-calculus/index.html) - Article.\n\n### Statistics\n\n- [Seeing Theory - Brown University](https://seeing-theory.brown.edu/) - Interactive book.\n- [Probability Cheatsheet - William Chen](https://github.com/wzchen/probability_cheatsheet) - Cheatsheet.\n- [Probabilistic Programming \u0026 Bayesian Methods for Hackers - Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Book.\n- [Computer Age Statistical Inference - Efron \u0026 Hastie](https://web.stanford.edu/~hastie/CASI/) - Book.\n\n## Computing\n\n### Python\n\n- [Learn Python: free interactive python tutorial](https://www.learnpython.org/) - Interactive tutorial.\n- [Python programming: learn python in one video - Derek Banas](https://www.youtube.com/watch?v=N4mEzFDjqtA) - Live coding.\n- [Python Magical Universe - Anna-Lena Popkes](http://alpopkes.com/portfolio/portfolio-3/) - Interactive course.\n- [Reproducible Data Analysis in Jupyter - Jake VanderPlas](http://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/) - Playlist.\n- [Microsoft Learn for NASA](https://docs.microsoft.com/en-us/learn/topics/nasa) - Tutorials.\n\n### R\n\n- [fasteR: fast lane to learning R! - Norman Matloff](https://github.com/matloff/fasteR) - Course.\n- [R for Data Science - Hadley Wickham](https://r4ds.had.co.nz/) - Book.\n\n### SQL\n\n- [Intro to SQL: Learn SQL for working with databases, using Google BigQuery to scale to massive datasets - Kaggle](https://www.kaggle.com/learn/intro-to-sql) - Interactive tutorial.\n- [Advanced SQL: Take your SQL skills to the next level - Kaggle](https://www.kaggle.com/learn/advanced-sql) - Interactive tutorial.\n- [The SQL Tutorial for Data Analysis - Mode](https://mode.com/sql-tutorial/introduction-to-sql/) - Tutorial.\n\n### NoSQL\n\n- [MongoDB Basics](https://university.mongodb.com/courses/M001/about) - Course.\n\n### Git\n\n- [The Git Parable](https://tom.preston-werner.com/2009/05/19/the-git-parable.html) - Article.\n- [Learn Git branching](https://learngitbranching.js.org/) - Interactive tutorial.\n- [Git Internals - Learn by building your own Git](https://www.leshenko.net/p/ugit/) - Interactive tutorial.\n\n### Cloud\n\n- [AWS Cloud Practitioner Essentials](https://www.aws.training/Details/Curriculum?id=27076\u0026scr=path-cp) - Course.\n\n## Machine Learning\n\n### Fundamentals\n\n- [Artificial Intelligence: a concise conceptual introduction - TDS](https://towardsdatascience.com/artificial-intelligence-d1e45efc99b4) - Article.\n- [A visual introduction to machine learning I - R2D3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) - Article.\n- [A visual introduction to machine learning II - R2D3](http://www.r2d3.us/visual-intro-to-machine-learning-part-2/) - Article.\n- [Making Friends with Machine Learning - Cassie Kozyrkov](https://www.youtube.com/playlist?list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG) - Course.\n- [Machine Learning Crash Course - Google](https://developers.google.com/machine-learning/crash-course) - Course.\n- [Machine Learning for Beginners - Microsoft](https://microsoft.github.io/ML-For-Beginners/#/) - Course.\n- [Introduction to Machine Learning for Coders - fast.ai](http://course18.fast.ai/ml) - Course.\n- [Practical Deep Learning for Coders - fast.ai](https://course.fast.ai/) - Course.\n- [The Elements of Statistical Learning - Hastie *et al*](https://web.stanford.edu/~hastie/ElemStatLearn/) - Book.\n\n### Data-specific resources\n\n#### Natural Language Processing\n\n- [Natural Language Processing: Distinguish yourself by learning to work with text data - Kaggle](https://www.kaggle.com/learn/natural-language-processing) - Interactive tutorial. \n- [A Code-First Introduction to Natural Language Processing - fast.ai](https://www.fast.ai/2019/07/08/fastai-nlp/) - Course.\n- [Text as Data - Chris Bail](https://cbail.github.io/textasdata/Text_as_Data.html) - Course.\n\n#### Computer vision\n\n- To do \n\n#### Time series\n\n- [Forecasting: Principles and Practice - Hyndman \u0026 Athanasopoulos](https://otexts.com/fpp2/) - Book.\n\n### Model-specific resources\n\n#### Neural networks\n\n- [Neural Networks - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) - Playlist.\n- [Neural Networks and Deep Learning - Michael Nielsen](http://neuralnetworksanddeeplearning.com/) - Book.\n- [Deep Learning from the Foundations - fast.ai](https://course.fast.ai/part2) - Course.\n\n### Application-specific resources\n\n#### Recommender systems\n\n- [How does Netflix recommend movies? Matrix Factorization - Luis Serrano](https://www.youtube.com/watch?v=ZspR5PZemcs) - Video.\n\n### Digging deeper\n\n#### Feature engineering\n\n- [Feature Engineering and Selection: A Practical Approach for Predictive Models - Max Khun](https://bookdown.org/max/FES/) - Book.\n- [Feature Engineering: Discover the most effective way to improve your models - Kaggle](https://www.kaggle.com/learn/feature-engineering) - Interactive tutorial.\n\n#### Interpretable Machine Learning\n\n- [Machine Learning Explainability: Extract human-understandable insights from any machine learning model - Kaggle](https://www.kaggle.com/learn/machine-learning-explainability) - Interactive tutorial.\n- [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar](https://christophm.github.io/interpretable-ml-book/) - Book.\n\n#### Other cool resources\n\nNewsletters:\n- [Data Elixir](https://dataelixir.com/): stay up to date in Data Science.\n- [The Batch](https://www.deeplearning.ai/thebatch/): what matters in AI right now.\n- [Alpha Signal](https://alphasignal.ai/): the best of Machine Learning. Summarized by AI.\n\nStranger things:\n- [Papers with code](https://www.paperswithcode.com/): reproducible research. Yay!\n- [ArXiv Sanity Preserver](http://www.arxiv-sanity.com/): keep your sanity while sifting through ArXiv. The 'top hype' tab is pretty cool.\n- [Artificial Intelligence Podcast](https://lexfridman.com/ai/): nice conversations about AI.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleandromineti%2Fml-journey","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fleandromineti%2Fml-journey","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleandromineti%2Fml-journey/lists"}